Definition
A knowledge graph is a structured representation of entities and their relationships, stored as nodes and edges in a graph database. It encodes real-world knowledge for reasoning and search.
Purpose
The purpose is to organize knowledge in a machine-readable way. It enables semantic search, recommendations, and reasoning over relationships.
Importance
- Improves search accuracy through context.
- Supports explainability in AI systems.
- Enables integration of structured and unstructured data.
- Requires ongoing updates to remain accurate.
How It Works
- Identify entities (people, places, concepts).
- Define relationships between entities.
- Populate graph with data from structured/unstructured sources.
- Store in a graph database with schema.
- Query graph for reasoning or search tasks.
Examples (Real World)
- Google Knowledge Graph: improves search relevance.
- Wikidata: open knowledge base for linked data.
- Microsoft Academic Graph: represents research publications.
References / Further Reading
- Hogan et al. “Knowledge Graphs.” ACM Computing Surveys.
- W3C RDF Standard.
- Google Knowledge Graph documentation.